Engineering Manager, Data

Skills

About the Role

Xsolla seeks a strategic, hands-on Engineering Manager to lead data and machine learning initiatives, oversee ad-tech and experimentation platforms, ensure scalable privacy-compliant infrastructure, and connect data science outcomes to product and business results.

Requirements

• 5+ years in software or data engineering or applied data science, including 3+ years managing technical teams in ML, analytics, or ad tech.
• Deep knowledge of machine learning and statistical modeling, including regression, classification, causal inference, uplift modeling, and forecasting.
• Hands-on experience with Snowflake, BigQuery, Spark, Airflow, dbt, MLFlow, and feature stores.
• Experience architecting and deploying batch and real-time ML systems into production.
• Knowledge of ad-tech ecosystems, attribution models, campaign hierarchies, and creative performance tracking.
• Familiarity with audience management, segmentation, and personalization frameworks.
• Experience with privacy-preserving measurement, SKAdNetwork, GAID/IDFA deprecation, and consent systems.
• Excellent leadership, communication, and stakeholder management skills.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field; PhD is a plus.

Responsibilities

• Lead and grow a high-performing distributed team of data scientists, ML engineers, and data platform engineers.
• Define and execute the data science and ad-tech roadmap across user modeling, campaign optimization, targeting, and personalization.
• Architect and manage ML pipelines and experimentation frameworks, including feature engineering, training pipelines, model serving, A/B testing, and causal inference.
• Oversee real-time ad-event pipelines for attribution and performance optimization.
• Collaborate with Product, Growth, and Marketing on audience scoring, LTV/churn models, and incrementality testing.
• Ensure scalable, privacy-compliant infrastructure aligned with GDPR, CCPA, and ATT, including SKAdNetwork, CMPs, and identity frameworks.
• Promote reproducibility, model evaluation, observability, and model lifecycle management.
• Translate data science insights into product and go-to-market outcomes.
• Mentor engineers and scientists on technical depth, career development, and leadership.

Benefits

• Medical, dental, and vision insurance
• PTO
• Personalized career roadmap
• Professional development through training and educational opportunities

Hiring Process

Background check including criminal history, employment verification, and education verification.